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整合 bulk 和单细胞分析描绘了胰腺癌中的神经酰胺代谢。

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer.

机构信息

Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Department of Visceral, Vascular and Endocrine Surgery, Martin-Luther-University Halle- Wittenberg, University Medical Center Halle, Halle, Germany.

出版信息

BMC Cancer. 2024 Nov 1;24(1):1347. doi: 10.1186/s12885-024-13114-8.

Abstract

BACKGROUND

Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.

METHODS

Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.

RESULTS

A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.

CONCLUSION

This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

摘要

背景

异常的鞘脂代谢(SM)与癌症的发生密切相关。然而,SM 在胰腺癌(PC)中的作用仍不清楚。本研究旨在探讨 SM 在 PC 预后、免疫微环境和治疗中的意义。

方法

通过 TCGA 和 GEO 数据库获取 PC 的单细胞和批量转录组数据。通过 MSigDB 数据库获取 SM 相关基因(SMRGs)。利用共识聚类构建 SM 相关分子亚型。LASSO 和 Cox 回归构建 SM 相关预后签名。ESTIMATE 和 CIBERSORT 算法评估肿瘤免疫微环境。OncoPredict 包预测药物敏感性。通过 CCK-8、划痕和 Transwell 实验分析 PC 细胞系 PANC-1 和 BxPC-3 中 ANKRD22 的功能。

结果

共获得 153 个 SMRGs,其中 48 个与 PC 患者的预后相关。在 PC 中鉴定出 2 个 SM 相关亚型(SMRGcluster A 和 B)。与 SMRGcluster B 相比,SMRGcluster A 的预后更差,SM 过程更活跃。免疫分析显示,SMRGcluster B 的免疫和基质评分以及 CD8+T 细胞丰度更高,而 SMRGcluster A 的肿瘤纯度评分以及 M0 巨噬细胞和活化树突状细胞丰度更高。具有 SMRGcluster B 的 PC 对吉西他滨、紫杉醇和奥沙利铂更敏感。然后构建了 SM 相关预后模型(包括 ANLN、ANKRD22 和 DKK1),该模型具有很好的预测性能。单细胞分析显示,在 PC 微环境中,巨噬细胞、上皮细胞和内皮细胞的 SM 活性相对较高。上皮细胞中 ANKRD22、DKK1 和 ANLN 的表达水平相对较高。高表达 ANKRD22、DKK1 和 ANLN 的细胞亚群具有更活跃的 SM 活性。体外实验表明,ANKRD22 敲低可抑制 PC 细胞的增殖、迁移和侵袭。

结论

本研究揭示了 SM 在 PC 中的重要意义,并鉴定了 SM 相关的分子亚型和预后模型,为 PC 患者的分层、预后预测和精准治疗提供了新的视角。

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